The role of AS3MT in arsenic metabolism has been described 
, and several prior studies have evaluated associations between candidate AS3MT variants arsenic-related traits in Bangladesh and elsewhere 
. A recent review 
highlighted two AS3MT SNPs, rs11191439 (Met287Thr) and rs3740393 (intronic), as being consistently related to arsenic metabolism across diverse populations. The most recent and comprehensive Bangladeshi study of AS3MT SNPs 
reported three association signals for arsenic metabolites, best represented by HapMap3 SNPs rs1046778 (for MMA%), rs11191439 (DMA% and iAs%), and rs3740390 (DMA% and iAs%), a proxy for rs3740393 (r2
0.91). After imputation, we were able to replicate rs11191439 (DMA% P
; MMA% P
) and rs1046778 (MMA% P
; DMA% P
0.0002), which were strongly correlated with lead SNPs rs4919694 (r2
0.69) and rs4290163 (r2
0.63), respectively. After conditioning on our lead SNPs, these associations were no longer significant. The evidence for rs3740390 was less convincing (DMA% P
0.54; MMA% P
0.007), as this SNP was not strongly correlated with any of our lead SNPs (Figure S10
). We identified two novel 10q24.32 association signals, represented by rs9527 and rs11191527, which were not strongly correlated with any previously-reported SNP (Figure S10
). These SNPs were likely missed in prior studies due to limited coverage of the SNPs in this region.
The identities of the functional variants in this region remain unclear. rs9527 lies in the 5′ UTR of C10orf32, a transcription factor binding region (GATA-1 and TAL1 (SC-12984)) and a DNase hypersensitivity site. If causal, rs9527 could also exert its effects through regulation of AS3MT-C10orf32 read-through transcription. However, the LD block represented by rs9527 includes transcription factor binding site SNP rs12416687 and miRNA SNPs rs11191401, rs12573077, rs7904252, and rs9527. Detailed information on potential functional variants from HapMap3 (GIH) for each of the 5 SNPs identified is contained in Tables S4
. However, genetic variation in this population has not been comprehensively characterized (especially rare variation), and the underlying functional variants may not be present in HapMap3. It is also possible that the underlying causal variants have implications for surrounding genes. For example, rs4919694 and rs11191527 are intronic SNPs within the CNNM2 gene, which is involved in magnesium reabsorption by the kidney 
. It is possible that magnesium and iAs interact 
, influencing the amount of free arsenic available for methylation.
To our knowledge, this study is the largest genetic association study of arsenic metabolites to date, the only GWAS of arsenic-related traits, the first study to implicate 10q24.32 SNPs in both arsenic metabolism and arsenical skin lesion risk, and one of the earliest GWAS conducted in the developing country setting. Our results suggest that MMA% and DMA% have distinct genetic determinants and highlight the importance of conditional analyses, as LD among alleles with opposing effects can mask associations in univariate analyses. The associations observed in this study are likely due to the effects of unmeasured, potentially rare variants in LD with the measured SNPs and/or substantial allelic heterogeneity, whereby multiple 10q24.32 variants influence arsenic metabolism.
Considering the substantial LD in this region 
, the variation in allele frequencies and LD patterns among the various arsenic-exposed populations under study 
, and the apparent allelic heterogeneity with respect to arsenic metabolism, future DNA sequencing studies are needed to help identify causal variants in the 10q24.32 region. Identifying these variants will help clarify the links between the association signals observed for %DMA, %MMA, and AS3MT/C10orf32 expression. These association signals appear largely independent in our dataset, but perhaps there are underlying causal variants that influence all of these phenotypes. Developing a better understanding the effects of functional variation related to AS3MT will also provide a more nuanced understanding of the biology of arsenic methylation, which can in turn help us better understand how variation in methylation efficiency affects health. Finally, knowledge of this causal variation and the methylation processes that they influence could potentially be exploited for intervention strategies that aim to prevent large numbers of deaths arsenic-exposed populations, by defining susceptibility subgroups and exploiting the biological processes uncovered by genomics for developing pharmacological treatments.